用快速模型预演原子运动，大幅加速分子动力学模拟。
Speculative Sampling For Faster Molecular Dynamics
- 引入扩散模型思路，用快模型预估下一步运动轨迹。
- 在不同系统上实现3-9倍加速，且不引入额外误差。
- 适合需要高效模拟的材料与生物分子研究者。
分子动力学（MD）是模拟原子系统动态行为的关键工具，但其固有的串行性限制了单系统吞吐量的提升。为此，我们提出兰之林推测动力学（LSD），一种分布式、模型无关的推测采样方法，可在不增加相对误差的前提下加速MD模拟。受语言与扩散模型中推测方法的启发，LSD利用一个快速的草稿模型预估模拟步骤，并在并行中由较慢的目标模型验证，通过从草稿分布到目标分布的传输映射实现校正。我们首次将推测采样扩展至二阶兰之林动力学，推导出速度提升与物理参数的关系，证明了LSD在不同系统及草稿-目标组合间具有泛化能力，实验显示其可实现3-9倍的速度提升，并理论与实证表明其生成的轨迹来自目标模型的真实分布。
原文摘要 · Abstract (English)
Molecular dynamics (MD) is a key tool for simulating the dynamical behavior of atomic systems. However, MD is inherently serial, which makes it difficult to increase single-system throughput with concurrent compute. To address this, we introduce Langevin Speculative Dynamics (LSD), a distributed and model-agnostic speculative sampler for accelerating MD without adding relative error. Inspired by speculative methods in language and diffusion modeling, LSD uses a draft model to propose fast simulation steps and verifies them in parallel with a slower target model, applying a transport map from the draft to the target distribution. We extend speculative sampling to second-order Langevin dynamics, derive the achievable speedup as a function of physical parameters, show that LSD generalizes across different systems and draft-target combinations with a 3-9x speedup, and confirm theoretically and empirically that LSD samples trajectories from its target model distribution.
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